Predicting the antecedents of trust in social commerce – A hybrid structural equation modeling with neural network approach

Lai Ying Leong, Teck Soon Hew, Keng Boon Ooi, Alain Yee Loong Chong

Research output: Journal PublicationArticlepeer-review

69 Citations (Scopus)

Abstract

Trust is an essential concern in s-commerce. Though existing research has studied the association between trust and purchasing intention; the determinants of the formation of trust in s-commerce remain largely unexplored. This study examines the determinants of trust in s-commerce based on social presence and social support. Unlike most business research, we applied a hybrid SEM-ANN approach that can detect non-linear and non-compensatory relationships. Linear and compensatory models assume that a shortfall in one factor may be compensated by other factors. However, consumer decision-making processes are complicated and non-compensatory and linear models tend to oversimplify these processes. Criterion sampling was used to gather 462 datasets of social commerce users using a mall intercept technique. Information support has the strongest effect followed by the social presence of interaction with the sellers, income and social presence of others. The integrated model predicts 76.9% trust in s-commerce. Theoretical and managerial contributions are discussed.

Original languageEnglish
Pages (from-to)24-40
Number of pages17
JournalJournal of Business Research
Volume110
DOIs
Publication statusPublished - Mar 2020

Keywords

  • Artificial neural network
  • Social Presence Theory
  • Social Support Theory
  • Social commerce
  • Trust

ASJC Scopus subject areas

  • Marketing

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